Generate: sequence bias can handle same terminations

src/transformers/generation/logits_process.py

@@ -624,9 +624,7 @@ def __init__(self, sequence_bias: Dict[Tuple[int], float]):
 
         # Bias variables that will be populated on the first call (for retrocompatibility purposes, the vocabulary size
         # is infered in the first usage, which inhibits initializing here)
-        self.sequences_length_greater_than_1 = []
         self.length_1_bias = None
-        self.length_greather_than_1_bias = None
         self.prepared_bias_variables = False
 
     @add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
@@ -642,11 +640,9 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> to
         bias += self.length_1_bias
 
         # 4 - include the bias from length > 1, after determining which biased sequences may be completed.
-        # `matching_mask` is a (batch_size, vocab_size) boolean mask that is True for all tokens whose corresponding
-        # bias should be applied. The bias is applied on the last token of the sequence, if (and only if) the sequence
-        # may become complete this iteration.
-        matching_mask = torch.zeros_like(scores, dtype=torch.bool)
-        for sequence_ids in self.sequences_length_greater_than_1:
+        for sequence_ids, sequence_bias in self.sequence_bias.items():
+            if len(sequence_ids) == 1:  # the sequence is of length 1, already applied
+                continue
             if len(sequence_ids) > input_ids.shape[1]:  # the sequence is longer than the context, ignore
                 continue
             prefix_length = len(sequence_ids) - 1
@@ -655,25 +651,18 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> to
                 input_ids[:, -prefix_length:],
                 torch.tensor(sequence_ids[:-1], dtype=input_ids.dtype, device=input_ids.device),
             ).prod(dim=1)
-            matching_mask[:, last_token] |= matching_rows.bool()
-        bias += torch.where(
-            matching_mask,
-            self.length_greather_than_1_bias,
-            torch.tensor(0.0, device=self.length_greather_than_1_bias.device),
-        )
+            bias[:, last_token] += sequence_bias * matching_rows
 
         # 5 - apply the bias to the scores
         scores = scores + bias
         return scores
 
     def _prepare_bias_variables(self, scores: torch.FloatTensor):
         vocabulary_size = scores.shape[-1]
-        sequence_bias = self.sequence_bias
-        tokens_with_bias = []
 
         # Check biased tokens out of bounds
         invalid_biases = []
-        for sequence_ids in sequence_bias:
+        for sequence_ids in self.sequence_bias:
             for token_id in sequence_ids:
                 if token_id >= vocabulary_size:
                     invalid_biases.append(token_id)
@@ -686,20 +675,9 @@ def _prepare_bias_variables(self, scores: torch.FloatTensor):
         # Precompute the bias tensors to be applied. Sequences of length 1 are kept separately, as they can be applied
         # with simpler logic.
         self.length_1_bias = torch.zeros((vocabulary_size,), dtype=torch.float).to(scores.device)
-        self.length_greather_than_1_bias = torch.zeros((vocabulary_size,), dtype=torch.float).to(scores.device)
-        for sequence_ids, bias in sequence_bias.items():
+        for sequence_ids, bias in self.sequence_bias.items():
             if len(sequence_ids) == 1:
                 self.length_1_bias[sequence_ids[-1]] = bias
-            else:
-                self.sequences_length_greater_than_1.append(sequence_ids)
-                if self.length_greather_than_1_bias[sequence_ids[-1]] != 0.0:
-                    raise ValueError(
-                        "Setting a bias on sequences that share a common token termination is not yet supported. "
-                        "Please open an issue if you see this error message (after checking that it doesn't already "
-                        "exist)."
-                    )
-                self.length_greather_than_1_bias[sequence_ids[-1]] = bias
-            tokens_with_bias.append(sequence_ids[-1])
 
         self.prepared_bias_variables = True